This paper discusses the result of the development of a hydrometeorological hazard early warning system (H-MHEWS) that combines weather prediction from Weather Research and Forecasting (WRF) and the hydrometeorological hazard index from the National Disaster Management Authority (BNPB), Indonesia. In its current development phase, the hazards that H-MHEWS predicts are floods, landslides, and extreme weather events. Potential hazard indices are obtained by using an overlay approach and resampling so that the data have a 100-m spatial resolution. All indices are classified into 4 status categories: "No alert", "Advisory", "Watch", and "Warning". Flood potential is produced by overlaying rainfall prediction at 3hour intervals with the flood index. Landslide potential is produced by overlaying rainfall prediction with the landslide index. Extreme weather potential is divided into 3 categories, i.e. heavy rain, strong winds, and extreme ocean waves. The whole prediction is dynamic, following weather predictions at 3-hour intervals. The hazard prediction results will trigger a 'Warning' alert in case of emergency status. This alert will be set up in a notification system to make it easier for the user to identify the most dangerous hydrometeorological hazard events.

Air pollution is one of the environmental problems that have a bad impact on human life. Air pollution sources come from various sources such as industry, mining, transportation activities, etc. This study aims to identify the seasonal dispersion of Sulfur Dioxide produced by kiln stacks in PT Semen Padang, Tbk. The Dispersion model used in this study is CALPUFF (For air pollution model) integrated with WRF (Weather Research Forecasting) (For meteorological model). CALPUFF requires the wind field generated by the CALMET model and the WRF model. From the simulation, the direction of SO2 dispersion on the wet season and the dry season is influenced by meteorological phenomena such as sea breeze and land breeze. The dispersion of Sulfur Dioxide in the dry season passes the urban area more than in the wet season, with the highest concentration is 122 µg/m3. On the other hand, in the wet season, the highest concentration is 120 µg/m3. These results are below of national quality standard in Indonesia (365 µg/m3). The statistic also shows that the correlation and error (RMSE) between the model and observed data are 0.63 and 5.49 in the wet season, whereas the correlation and error (RMSE) in the dry season are 0.74 and 2.89.

Sumatran Fault is one of hazard located in Sumatra Island. Southern segment of Sumatran fault is one of sources of earthquakes in Lampung Province. Hazard map is used as consideration in developing region. The source of hazard comes from stress accumulation of crust which can be derived from movement of points in surface. The study of points or monitoring bench mark to accommodate more precise fault movement or slip and stress accumulation is important for sustainable development in Lampung Province. Tool used for analysis is geographic information system especially buffer analysis. Available monitoring bench mark is analysed so that each bench mark can be classified based on its contribution of fault movement based on distance from segment of current fault. High class bench marks are prioritized to be used as survey sites to monitor fault movement. The other analysis is analysis to obtain region that lack bench mark to monitor segment of current fault or even discover new fault which is branch of segment of current fault. The result of this research is there are four high class bench marks. 58.54% of total segments of 164.020 km long Sumatran fault in Lampung Province is segments with no monitoring bench marks. Three most possible district to build bench mark are Airhitam, Lemong, and Ngambur District.

Introduction: Indonesia is ranked the 4th most populous country in the world. Since Covid19 is highly transmissible from human to human, Indonesia might suffer a long period of the Covid19 pandemic than other less-populous countries. This study aimed to find the correlations of tropical climate, population density and confounding factors with Covid19 progression in Indonesia from March to August 2020. Methods: The climatological data, population density, laboratory testing, and the confirmed Covid19 cases were statistically analyzed. The correlations between each data were performed with Pearson’s Correlation Coefficient using a Statistical Package for the Social Sciences. The values of statistical significance were considered at 95% and 99% confidence intervals. Results and Discussion: Indonesia recorded more than 1,315 confirmed Covid19 cases in almost all provinces (30 out of 34) during the dry season (March to August 2020). During the early pandemic, DKI Jakarta and East Java have been the epicenters of the pandemic in Indonesia. Humidity and precipitation have a weak negative correlation, while the temperatures have a weak positive correlation. Population density and laboratory testing have a strong positive and significant correlation with the cumulative confirmed Covid19 cases. Conclusion: Our study indicates that tropical climate less affects the cumulative Covid19 case in Indonesia than population density and laboratory testing capacity.

The population growth with its activities causes pressure on the Krukut River. Load management of pollutants that enters the river is based on the self purification of the river. This study aims to analyze the river characteristics and degradation rate of Krukut River. Krukut River which is a research location has a length of ± 9.04 km. Characteristics of Krukut River has a type of small rocky riverbed and irregular with manning coefficient from 0.035 to 0.045. The depth ranging from 0,99 - 2 m, with a current velocity from 0,3 to 1,29 m/s. Krukut River discharge at 2,873-7,889 m3/sec. Streeter Phelps modeling to find out the constant rate of degradation of Krukut River which resulted in the value of DO and BOD. The rate of increase of dissolved oxygen (Ka) with the value of Ka range of 1,586-4,542 d-1, the standard value should be 1,494 d-1. The results of degradation rate (Kd) of the study obtained values range 0,285–0,394 d-1 with a default value of 0,501 d-1. For a settling rate (Ks) the range of 0,070 d-1 –0,096 d-1 with a standard value should be 0,751 d-1 which means the precipitation process is quite slow. Keywords: pollution degradation rate, Krukut River, Streeter Phelps methode, selfpurification, water quality